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LLNL ECP Application Development

The Department of Energy’s Exascale Computing Project (ECP) Application Development focus area has the mission of delivering scalable science and mission performance on a suite of ECP applications that are ready for efficient execution on the ECP exascale systems. In addition, this focus area is responsible for Developer Training and Productivity. LLNL helps lead the Application Development focus area and its researchers participate in several ECP application projects.

The Application Development focus area also manages the ECP Co-Design Centers, which are loosely organized around scientific computing motifs, such as adaptive mesh refinement and particle methods. Working with the ECP applications projects, the goal of these centers is to accelerate the development of advanced, cross-cutting algorithmic techniques suitable for exascale computing.

LLNL Led

High Order Multi-physics for Stockpile Stewardship

Principal Investigator: Robert Rieben, LLNL

LLNL will advance multi-physics simulations of High Energy-Density Physics (HEDP) experiments driven by high-explosive, magnetic, or laser based energy sources. This project is developing support for the use of high-order numerical methods and multiple diverse algorithms for each major physics package in multi-physics codes. An additional goal is to improve end-user productivity for overall concept-to-solution, including more robust simulations, improved setup and meshing, in-situ vis and post-processing, and optimized workflow.

Partner Led

The DOE laboratories are extending their subsurface modeling capabilities by investing in the prediction of non-isothermal multiphase fluid flow and reactive transport coupled with geomechanics and fracture, and of induced seismicity and reservoir performance for energy storage and recovery applications. The primary focus is to gain greater understanding of the relevant mechanisms and gain a predictive capability of subsurface processes such as wellbore integrity and fracture evolution over scales ranging from the pore scale to kilometer scale. This project is a collaboration with LBNL and NETL.

CANDLE: Cancer Distributed Learning Environment

Principal Investigator: Rick Stevens, ANL; Fred Streitz, LLNL Lead

The DOE laboratories are drawing on their strengths in HPC, machine learning, and data analytics, and coupling those to the domain strengths of the National Cancer Institute (NCI), particularly in cancer biology and cancer healthcare delivery, to bring the full promise of exascale computing to the problem of cancer and precision medicine. CANDLE is endeavoring to build the software environment for solving very large-scale distributed learning problems on the DOE Leadership Computing platforms. The project aims to facilitate the use of these platforms for solving machine learning problems with a focus on three challenges in cancer research: (1) predicting the response of drugs from cancer cells, (2) analyzing cancer medical records to determine diagnosis and information from unstructured text that could be used to build models for large-scale population response to cancer treatment, and (3) probing the inner workings of cancer biology to help manage simulations of problems and protein mutations associated with cancer cells. This project is a collaboration with ANL, LANL, ORNL, NIH/NCL.

In this ECP application development project, we will take full advantage of DOE leadership in high performance computing and simulation science, within the construct of a multidisciplinary team, to develop an unprecedented computational toolset for earthquake hazard and risk assessment. The effort will build upon a significant body of computational developments from the national laboratories and contributing universities and position the earthquake hazard and risk domain to exploit emerging exascale platforms.

ES3M-MMF: Cloud-Resolving Climate Modeling of the Earth's Water Cycle

Principal Investigator: Mark Taylor, SNL; David Bader, LLNL Lead

In earth system modeling, there is a need for fully coupled global modeling of the atmosphere and ocean circulation, the evolution of sea ice and land ice, and the hydrology-soil-vegetation model of the land surface. Exascale computing will enable such a capability with sufficient process realism to capture all major climate forcings and feedbacks. This project is a collaboration with SNL, ANL, LANL, ORNL, PNNL, UC Irvine, and Colorado State Univ.

Oak Ridge, Lawrence Livermore, and Los Alamos National Laboratories and the National Institute of Standards and Technologies have partnered to create an Exascale Computing toolkit for Additive Manufacturing (AM). Using an Integrated Computational Materials Engineering (ICME) approach, the Exascale AM (ExaAM) project will develop and deploy a new multi-physics modeling and simulation toolkit able to provide an up-front assessment of the manufacturability and performance of additively manufactured parts. This Integrated Platform for AM Simulation (IPAMS) will be validated through a series of experimentally realizable demonstration problems and will use in-memory coupling between continuum and mesoscale models to quantify microstructure development and evolution during the AM process. It is this microscopic structure that determines local material properties such as residual stress and leads to part distortion and failure. The validated AM simulator will enable the determination of optimal process parameters for desired material properties, ultimately leading to reduced-order models that can be used for real-time in situ process optimization. When coupled to a modern design optimization tool, IPAMS will enable the routine use of AM to build novel and qualifiable parts.

QMCPACK: A Framework for Predictive and Systematically Improvable Quantum‐Mechanics Based Simulations of Materials

Using the full concurrency of exascale systems, quantum mechanics-based materials modeling would allow scientists and engineers to find, predict, and control materials and properties at the quantum level with an unprecedented and systematically improvable accuracy. This project is a collaboration with ORNL, ANL, SNL, and Stone Ridge Technology.

With today’s supercomputers, we can only simulate components of magnetically confined fusion reactors. The goal of this project is to bring together these component simulations to realize a whole device model with unprecedented fidelity including the evolution and coupling of multi-scale turbulence and background plasma and integrating the effects of energetic particles, extended MHD instabilities, plasma-surface interactions, and current drive. This project is a collaboration with PPPL, ANL, ORNL, Rutgers, UCLA, and Univ. Colorado.

Operated by Lawrence Livermore National Security, LLC, for the
Department of Energy's National Nuclear Security Administration.